Computing f-Divergences and Distances of High-Dimensional Probability Density Functions - Low-Rank Tensor Approximations.
Alexander LitvinenkoYoussef M. MarzoukHermann G. MatthiesMarco ScavinoAlessio SpantiniPublished in: CoRR (2021)
Keyphrases
- low rank
- probability density function
- high dimensional
- high dimensional data
- trace norm
- high order
- low dimensional
- tensor decomposition
- dimensionality reduction
- class conditional
- distance function
- linear combination
- missing data
- singular value decomposition
- low rank matrix
- convex optimization
- matrix completion
- nearest neighbor
- density function
- matrix factorization
- semi supervised
- density estimation
- similarity search
- higher order
- gaussian mixture model
- euclidean distance
- gaussian mixture
- bayesian framework
- kullback leibler divergence
- probability distribution
- data points
- statistical methods
- em algorithm
- distance measure
- principal component analysis
- mixture model
- expectation maximization
- data mining
- unsupervised learning
- pairwise
- feature space